We observed that the sampled b's (about 0.1) are indeed small for the four subsets, which shows there is no topic sharing among segments.

tSM is able to recognise the segments are unrelated text.


Although word error rate (WER) is commonly used in speech recognition, it considers accuracy and completeness, not readability.

a lower WER does not always result in better understanding (Wang et al., 2003).


At each vertex, we need to minimize the cost over all its 2 K − 1 predecessor nodes, and, for each such transition, we need to estimate the SOP objective function that requires O(K 2 ) operations.

the dynamic programming algorithm has time complexity of O(K 2 2 K N K ) and space complexity of O(N K ), which is infeasible for most practical problem instances.


As the window size increases, the accuracy of our MSA based combining system increases, but so does the computational cost and latency.

it is important to apply MSA with a relatively small window size for real-time captioning applications.


Given K sequences of length N each, we can have O(N K) distinct words.

the number distinct states is O(N b K (N K) 2 ) i.e.


One difference is that our system takes a hierarchical approach in that it aligns at the word level, but also uses string edit distance at the letter level as a substitution cost for words.

it is able to take advantage of the fact that individual transcriptions do not generally contain arbitrary fragments of words.


In this work, we frame the task of detecting story elements as a tagging task.

our problem reduces to assigning a tag to each word position in the retelling, the tag indicating the story element that the word is associated with.


First, when tagging confusion networks, each word in the network at position i results in a feature instance.

if there are five confusable words at position i, then there will be five different x i values being used to instantiate the features in Table 1.


Our features include the current tagger label with (1) the current predicted word-alignment label; (2) the previous predicted label; and (3) the next predicted label.

the new features were y i z i , y i z i−1 and y i z i+1 .


While these could be improved with targeted effort, we consider this a realistic application scenario, where in-domain training data is typically scarce, at least early in the development process.

any lexically based AD methods need to be robust to poor ASR accuracy.


For example, if we have an utterance How is the weather in Paris?, the addressee probabilities are likely to be similar had we seen London instead of Paris.

replacing words with properly chosen word class labels can give better generalization from the observed training data.


However, English-Spanish is our target language pair as our internal projects are cater mostly to this pair.

we created parallel text for English-Spanish based on the reference English segments released as part of the evaluation (Cettolo et al., 2012).


In a simultaneous speech translation system, one can neither find the optimal segmentation of the entire talk nor tolerate high latencies associated with long segments.

it is necessary to decode the incoming audio incrementally as well as segment the ASR hypotheses appropriately to maximize MT quality.


This may be attributed to the fact that the system is a pipeline consisting of ASR, segmenter, and MT, and that 3,000 ms is roughly the length of time to recite comma-separated chunks.

the two latency peaks appear to correspond with ASR producing segments that are most divergent with segments that the segmenter produces, leading to the most pipeline "stalls."


The result that we obtain is different, due to the fact that (Kupsc and Abeillé, 2008) have a more abstract definition of SF.

they define a smaller number of SF: 58 instead of 666 in our case.


The results of applying function ψ R were more successful.

we just report the results of this function in table 6.


First, since any one feature is likely to occur infrequently, a large amount of training data is necessary to reliably estimate their weights.

we use the full parallel training data (rather than a small development set) to estimate the contribution of the sparse features in phase 1.


We found that very similar contexts are among the top 5 ranked results of "graph" while no such contexts appear in that of the other two words.

we define a context-aware term informativeness based on the semantic relatedness between the context and the term's featured contexts (or the top important contexts that cover most of a term's semantics).


This suggests that if term t a in context c is more informative than t b , then most likely the contexts from t a 's featured context set will be more related to c than will t b .

given a term t and its featured context set U f (t) = {c 1 , ..., c k }, we define the term informativeness of t in context c i as where κ(c i , c j ) is the semantic relatedness of c i and c j , which can be computed by various semantic relatedness metrics such as Wikipedia based (Gabrilovich and Markovitch, 2007;Yazdani and Popescu-Belis, 2012), Wordnet based (Agirre et al., 2009;Budanitsky and Hirst, 2006), or simple cosine similarity and Jaccard similarity based (Zobel and Moffat, 1998).


For example, in "pattern recognition and machine learning" by Bishop, "hidden Markov model" appears in more than 20 pages while the actual index entry has only 2 pages as its locators.

the actual problem is to identify a index term with its context.


The numbers above are interesting because they provide intrinsic evaluation of the concept induction procedure, but they do not tell us much about their usability.

and in order to better assess the value of the discovered concepts, we decided to carry out two extrinsic evaluations using an information extraction task.


However, naïve application of query-level parallelism leads to a large number of wasted threads, since most long substrings of an input sentence will not be found in the text.

we employ a novel two-pass strategy: in the first pass, we simply compute, for each position i in the input sentence, the length j of the longest substring in F that appears in T .


Input: D, Φ, minibatch size m, and # of processors p Output: in serial until converged other examples in the same batch.

we can easily distribute decoding for different examples in the same minibatch to different processors.


Instead, we would like to directly analyze the syntax of online conversational text on its own terms.

we choose to leave these word forms untokenized and use compound tags, viewing compositional multiword analysis as challenging future work.


Results show a similar pattern as the Twitter data (see final column of Table 2).

the Twitter word clusters are also useful for language in the medium of text chat rooms; we suspect these clusters will be applicable for deeper syntactic and semantic analysis in other online conversational text mediums, such as text messages and instant messages.


Due to their diversity, they cannot always be found in parallel corpora, dictionaries or gazetteers.

state-of-the-art of MT needs to handle NEs in specific ways.


We also develop the features for the MRF model in such a way that the resulting model is of the same format as that of a traditional phrase table.

the model can be easily incorporated into a standard phrase-based SMT system, requiring no code change in the runtime engine.


So we argue that normalization followed by regular translation is a more practical approach.

text normalization is important for social media text processing.


We also believe that punctuation correction is closely related to sentence boundary detection.

we propose a two-layer DCRF model for punctuation correction.


If an informal word is a concatenation of two constituent informal words w 1 and w 2 in our normalization dictionary, the informal word will be segmented into two words w 1 and w 2 .

the Dictionary hypothesis producer can subsequently normalize w 1 and w 2 .


In the formulation of the inside-outside algorithm based on tensor T , each vector α i,j and β i,j consists of m elements, where computation of each element requires time O(m 2 ).

the algorithm has a O(m 3 ) multiplicative factor in its time complexity, which we aim to reduce by means of an approximate algorithm.


The distributions of errors in the three test sets are not similar, and therefore, the stability in performance of the WikiRev systems cannot be attributed to the hypothesis that the WikiRev training data error distributions are more similar to the test data than any of the other training corpora.

we claim that if a preposition error correction system is to be deployed on data for which the error characteristics are not known in advance, i.e.


A Sense is a discrete representation of a single aspect of the meaning of a word.

a dictionary entry is of the form <LexicalUnit, In this section, we propose a series of algorithms, each one of which automatically creates a reverse dictionary, or ReverseDictionary, from a dictionary that translates a word in language L 1 to a word or phrase in language L 2 .


Although formally syntactic, such models rarely respect linguistically-motivated syntax, and have no formal notion of semantics.

they tend to produce translations containing both grammatical errors and semantic role confusions.


In contrast, our hard constraints have already filtered out dominating nonintuitive phrases; thus there is more room to include additional useful phrases.

we can switch off the constraints on initial phrase length in both training and decoding.


In both training and decoding, we require the phrases covered by T to satisfy our syntactic constraints.

all translation nodes (both Xs and T s) in derivations are syntactically informed, providing room to explore PAS reordering in HPB model.


Word sense annotation is regarded as one of the most difficult annotation tasks (Artstein and Poesio, 2008) and building manually-annotated corpora with highquality sense labels is often a time-and resourceconsuming task.

nearly all sense-tagged corpora in wide-spread use are created using trained annotators (Hovy et al., 2006;Passonneau et al., 2010), which results in a knowledge acquisition bottleneck for training systems that require sense labels (Gale et al., 1992).


However, many agreement studies have restricted annotators to using a single sense, which can significantly lower inter-annotator agreement (IAA) in the presence of ambiguous or polysemous usages; indeed, multiple studies have shown that when allowed, annotators readily assign multiple senses to a single usage (Véronis, 1998;Murray and Green, 2004;Erk et al., 2009;Passonneau et al., 2012b).

we focus on annotation methodologies that enable workers to use as many labels as they feel appropriate, asking the question: if allowed to make labeling ambiguity explicit, will annotators agree?


Second, many studies using crowdsourcing combine the results into a single answer, thereby leveraging the wisdom of the crowds (Surowiecki, 2005) to smooth over inconsistencies in the data.

in the second experiment, we evaluate different methods of combining Turker responses into a single sense labeling, referred to as an aggregate labeling, and comparing that with the reference labeling.


Speech is a major component of modern user interfaces as it is the natural means of human communication.

it seems logical to use Spoken Dialogue Systems (SDS) as part of Intelligent User Interfaces enabling speech communication of different complexity reaching from simple spoken commands up to complex dialogues.


It is defined by where x i and y i are corresponding ranked ratings andx andȳ the mean ranks.

two sets of ratings can have total correlation even if they never agree.


While LVM training makes sequential scans of the corpus (which can be stored on disk), it requires consistent random access to model parameters.

the model parameters must be stored in memory on each node.


In fact, for popular learning techniques including EM for HMMs (Rabiner, 1989) and variational EM for LDA (Wolfe et al., 2008), it can be shown that the parameter updates are independent of how the corpus is partitioned.

for those approaches our partitioning is guaranteed to produce the same models as any other partitioning method; i.e., model accuracy is unchanged.


Lastly, we note that we target synchronized LVM training, in which all nodes must finish each training iteration before any node can proceed to the next iteration.

we desire balanced partitions to help ensure iterations have similar durations across nodes.


Second, we found that document selection has relatively minor impact on memory footprint, providing a roughly 5% incremental benefit over random document selection.

although we utilize BATCH DISSIMILARITY in the final system we evaluate, simple random document selection may be preferable in some practical settings.


By contrast, training a 200-state HMM on the same corpus requires over a hundred CPU-days.

bJAC's time to partition has a negligible impact on total training time.


We are thus faced with a large number of ways to spell the same word, none of which can be assumed as "standard" since there is no standard.

the increased data sparseness adds to the challenges of NLP tasks such as machine translation, compared to languages for which orthography is standardized.


The first classifier, corresponding to the different A forms, determines the transformation of A to Â, while the / h/h classifier determines the correction of h to h. The other classifiers are either not involved since they do not work on any of the word characters, or they determine that no character transformation should happen for this word.

applying the CEC technique in this case changes the word Arbςh to Ârbςh, which is the correct CODA form.


Although MADA ARZ is originally developed to work as a morphological tagger, it still can help the codafication process, since the choice of a full morphological analysis for a word in context determines its CODA spelling.

mADA ARZ is able to correct many word misspellings that are common in spontaneous orthography.


Indeed, an ambiguous respelling is compatible with at least two pronunciations, only one of which can be the intended pronunciation.

for a given reader, unambiguity is a necessary but not sufficient condition for correctness.


, m], the alignment to the source and its translation is independent of all other translation and alignment decisions.

the probability that the ith word of e is e i can be computed as: We can also compute the posterior probability over alignments using the above probabilities, Finally, since all words in e (and their alignments) are conditionally independent, 3 Evaluating and maximizing the data likelihood under log-linear models can be computationally expensive since this requires evaluation of normalizing partition functions.


This differs from the standard IN-FA-IN by that we have more phrase pairs to use for FA.

we obtain phrase pairs relevant to IN in addition to "general" phrase pairs which were not extracted from IN, perhaps due to faulty word alignments.


Typically, LDA iterates over the corpus until a convergence requirement is met, but in this case this is neither practical (due to the size of our corpus) nor necessarily desirable; the diminishing effects of the initial seeding means that the model may not stabilize, in terms of its likelihood, until after it has shifted away from our desired stylistic dimensions towards some other variation in the data.

we treat the optimal number of iterations as a variable to investigate.


Second, multiple metadata attributes could impact the classification decision, and picking a single one might reduce classification accuracy.

we seek multi-domain learning algorithms which can simultaneously learn from many types of domains (metadata attributes).


However, this conflicts with the original CW intuition that features with higher variance (lower confidence) should receive higher updates; since they are more in need of change.

we implemented a modified "variance" scheme, where the updates are dis-tributed to the underlying classifiers such that higher variance features receive the larger updates.


Letting the probability of a word appearing in a retelling be p 1 and the probability of that word appearing in all other retellings be p 2 , we can express the odds ratio as follows: A large tf-idf or log odds score indicates that the word j is very specific to the retelling i, which in turn suggests that the word might be unexpected or inappropriate in the larger context of the NNM narrative.

we expect that the words with higher association measure scores are likely to be the words that were manually identified as unexpected in the context of the NNM narrative.


Topic-specific words ought to occur much more frequently than other words.

words with high tf-idf and log odds scores are likely to be those unrelated to the topic of the NNM story.


Since prosodic phrase boundaries tend to occur in locations that coincide with certain syntactic constituent boundaries, it is possible to automatically predict prosodic phrase boundary locations from part-of-speech labels and syntactic structure with fairly high reliability for read news stories (Ananthakrishnan and Narayanan, 2008).

we hypothesize that we can more effectively leverage word-level articulation and pause information by focusing on words that are less likely to be associated with prosodic phrase boundaries.


We make use of the speaking rate model to adjust the speaker-independent TIMIT phone durations to the speakers in the FAN corpus by calculating the linear scale factor α for each speaker.

the phone mean and standard deviation used in the z-score normalization is αµ p i and ασ p i , respectively.


The correlation was much lower for sentence boundaries (0.44).

we conclude that the length of pauses at non-boundary locations is related to the speaker's reading ability.


However, larger data sets come with increased demands on computational resources; moreover, they tend to include redundant information as their size increases.

the performance gain curves of large-scale systems with respect to the amount of training data often show "diminishing returns": new data is often less valuable (in terms of performance gain) when added to a larger pre-existing data set than when added to a smaller pre-existing set (e.g., (Moore, 2003)).


If we don't consider the ranks coming from NO-LM, the average rank of syllable-and morph-based models become 1.1 and 1.8, respectively.

we use syllable-based models over the others for the rest of the experiments.


The graphical model is illustrated in Figure 2b.

oil will receive more resource topic from crude through the bigram crude oil, instead of only finance topic from the sentence as a whole.


As pointed out by the original proposers, one of the main advantages of these models is that the distributed representation achieves a level of generalization that is not possible with classical n-gram language models; whereas a n-gram model works in terms of discrete units that have no inherent relationship to one another, a continuous space model works in terms of word vectors where similar words are likely to have similar vectors.

when the model parameters are adjusted in response to a particular word or word-sequence, the improvements will carry over to occurrences of similar words and sequences.


The baseline for NU annotations shows that negations are scarce in these RTE datasets, which was also the case for ct-and pt-annotations.

table 2 mostly indicates truthteller's performance in distinguishing positive Ct and Pt annotations from unknown ones, the latter constituting 20% of the gold standard.


In contrast, for our method based on hitting times, the performance continually improves as the seed set size is increased.

also in terms of learning performance, our method is more sound than iterative bootstrapping.


In this paper, we show that a significant number of "negative" examples generated by the labeling process are false negatives because the knowledge base is incomplete.

the heuristic for generating negative examples has a serious flaw.


constant so that the only unknown variable is who does the internal processing" (Paek, 2001).

hiding the human wizard's input by layers of system interface can render that system believably automated.


As demonstrated in Section 2.2, the collapsed average coherence score can be used to improve the performance of an automated speech scoring system.

this study treats coherence prediction as a binary classification task: low-coherent vs. high-coherent, where the low-coherent responses are those with average scores 1 and 1.5, and the high-coherent responses are those with average scores 2, 2.5, and 3.


It does this by leveraging the equational inference capabilities of the regular expression domain, making it a form of semantic unification.

the contribution of our work is to show that using semantic unification to find a deeper level of equivalence helps to disambiguate language meanings.


The first set is all of the quoted string literals in the natural language phrases from the training set.

for the phrase, "lines with 'bob' twice" we would add the lexical entry 'bob', R:bob .


For example, bomb > nsubj (subject argument of bomb) often indicates a PERPETRATOR.


For example, TAC slots such as WHY and COUNTERMEASURES likely correspond to multiple slots at the granularity of MUC.

we also consider mapping the N -best system-induced slots to each TAC slot, for N up to 5.


Its complex conjugate c * = r e −iθ has the inverse phase.

their product cc * = (r e iθ )(r e −iθ ) = r 2 is real.


In fact the decomposition where the p i sum to 1 and the |s i are mutually orthogonal is unique and is called the eigen decomposition.

b eig = {|s i } i constitutes an orthonormal base, ρ's so-called eigen base.


Question-type Chunking features do not capture the connection between question word and answer types.

they have to be combined with question types.


A word being aligned suggests it is less likely to be an answer.

for each word we extract features based on its edit type, shown in Table 4.


Here we use a single general feature that we expect to pick up much of this signal, without the significant feature engineering.

we incorporated a simple feature to roughly model this phenomenon.


Finally, we found it difficult to do a head-to-head comparison with other QA systems on this task.

12 we contribute this dataset to the community, hoping to solicit direct comparisons in the future.


With these notations, RD(q, s) is true if and only if is true for some w h , w d |q , for some u h , u d |s and for some i and j. EQ(w h , u h )∧EQ(w d , u d ) requires that the question dependency w h , w d |q and the snippet dependency u h , u d |s match; w h ∈ m (i) ∧ w d ∈ m (j) requires that the head word and dependent word are in the i th -rank and j th rank MMP, respectively.

rD(q, s) is a dependency feature enhanced with MMPs.


A meeting is generally accomplished by a number of participants and a wide range of subjects are discussed.

it would be helpful to meeting participants to provide them with some additional information related to the current subject.


Many studies recently tried to extend them by incorporating specific information such as linguistic knowledge (Hulth, 2003), web-based resource (Turney, 2003), and semantic knowledge (Chen et al., 2010).

they show good performance on written text.


We assume that only the preceding utterances that are directly related with the current utterance are important for extracting keywords from the current utterance.

a subgraph of G 2 that maximally covers the current utterance graph (G 1 ) is extracted.


In general, many participants discuss a wide range of topics in a meeting.

some preceding utterances that shares topics with the current utterance are more significant.


As stated above, the weights of the edges in the history graph G 2 are updated by Equation (2).

before merging G 5 and G 2 , all weights of G 2 are updated by increasing t as t + 1 to reflect temporal importance of preceding utterances.


Even though all congressmen in the transcript belong to the same committee, they discussed various topics at the meeting.

the keywords are difficult to be agreed unanimously by all three 2 The data set is available: http://ml.knu.ac.kr/ dataset/keywordextraction.html 3 A guideline was given to the annotators that keywords must be a single word and the maximum number of keywords per utterance is five.


TextRank model achieves F-measure of 0.478 and weighted relative score of 0.387, while TFIDF reports its best F-measure of 0.481 and weighted relative score of 0.394.

the difference between TFIDF and TextRank is not significant.


Since the proposed method uses, as history, the preceding utterances relevant to the current utterance, its performance is kept high even if whole utterances are used.

it could be inferred that it is important to adopt only the relevant history in keyword extraction from meeting transcripts.


This variant and the proposed method both rely on the temporal history, but the difference is that the history graph of the variant accumulates all information within the topic segment.

the keywords extracted from the history graph by this variant are those without consideration of topic relevance.


However, the proposed method still outperforms the variant, and it demonstrates the importance of topic relevance.

it can be concluded that the consideration of temporal history and topic relevance is critical in keyword extraction from meeting transcripts.


The ratio will be even worse with a larger window size.

usually a fixed window size is used restricting the set of candidates.


Assuming that e 2 is in e 1 's cause argument, we can infer that e 2 occurs Before e 1 since intuitively the cause of an action precedes the action.

we create features for temporal relation classification based on four types of PropBank-style predicate-argument relations, namely directional, manner, temporal, and cause.


In early SMT systems, such as (Koehn, 2004), changes in word order when a sentence is translated were modeled by means of a penalty that is in-curred when the decoder chooses, as the next source phrase to be translated, a phrase that does not immediately follow the previously translated source sentence.

the system penalizes deviations from monotone order, with the magnitude of the penalty being proportional to distance in the source sentence between the end of the previously translated source phrase and the start of the newly chosen source phrase.


A block is the largest contiguous sequence of phrase pairs that satisfies the phrase pair consistency requirement of having no external links.

classification of the orientation of a newly chosen phrase as M, S, or D is carried out as if the decoder always chose the longest possible source phrase in the past, and will choose the longest possible source phrase in the future.


We show a reference translation of the Chinese source (not found in the comparable data) that reorders the phrases as 1, 3, 2.

rM adaptation allows the rM to learn that certain corpora whose reordering information is of lower quality corpora should have lower weights.


This is that 0 probabilities are assigned to all orientations whenever a phrase pair is absent from a particular sub-corpus.

for example, a pair (f, e) that occurs only in sub-corpus i will receive a probability p(o|f, e) = α i p i (o|f, e) in the mixture model (equation 2).


weights obtained at each iteration are likely to be best for the metric that was optimized in that iteration.

one could use weights from the last k iterations (for lateen-tuning with as many metrics) and then decode the test set with an ensemble of these weights as in PMO ensemble.


The best child reveals more words, so the score may go up or down when the language model is consulted.

simply following best children may lead to a poor hypothesis.


In our approach we model these distributions using hierarchical PY processes instead of the categorical distributions.

we place the following assumptions on IBM model 1: In this probability modelling we assume that the alignment positions are determined using the uniform distribution, and that word translations are generated depending on the source word -the probability of translating to a specific foreign word depends on the observed frequency of pairs of the foreign word and the given source word.


Our concept graph does not have this constraint: at any level a word can be associated with an arbitrary number of parent clusters.

not only HGFC outperformed agglomerative clustering methods in hi-erarchical clustering tasks (Yu et al., 2006;Sun and Korhonen, 2011), but its hierarchical graph output is also a more suitable representation of the concept graph.


The metaphor identification system of  starts from a small seed set of metaphorical expressions, learns the analogies involved in their production and extends the set of analogies by means of verb and noun clustering.

the system can recognize new metaphorical expressions in unrestricted text (e.g.


Naturally, some of these pairs were better examples than the others.

in the second phase, the goal was to measure the degree of their similarity to the corresponding relation.


Still, many candidates are generated (such as "really") for those tweets, given that those candidates can still potentially link to an entity ("really" could be a TV channel).

it is very important to include tweets without entities in the training set because we do not want our system to create unnecessary links to entities.


For example, we examined the annotation used in (Meij et al., 2012) and found it to be extremely lenient, when compared to our own beliefs of what is an entity.

we believe evaluating performance on restricted entity types is the only fair way to compare different endto-end entity linking systems.


However, these existing studies are not based on interdependent documents like threaded forum posts.

at least two important characteristics of threaded forum data are not considered in these models.


But they assume that documents are already grouped by viewpoints, which is not the case for forum posts.

their model cannot be directly applied to forum posts.


This is understandable (as we discussed earlier) because most words and sentences in both intention and nonintention posts are very similar.

feature selection is highly desirable for this application.


This is because the best performing semantic role labeling systems rely on syntactic features extracted from automatic parses (Gildea and Palmer, 2002;Punyakanok et al., 2005) and the parsers that produce them do not generally reproduce empty categories.

current semantic role labeling systems can only recover explicit arguments.


For instance, benefactor is a suitable pseudosense for both senses of donor, whereas philanthropist cannot be used in the blood donation sense.

we carried out another manual evaluation to test the efficacy of pseudowords in preserving the distinguishability of senses of real words.


If the model has too many parameters, unsupervised learning can easily pick up on and exploit coincidental correlations in the data.

we make a modeling assumption that keeps our parameterization simple.


When we looked only at pairs more than two points different, the agreement was 80%.

given the subjective nature of the task, we feel reasonably confident that the AMT annotations are informative, and that the dramatic preference of G-FLOW over the baseline systems is due to a substantial improvement in its summaries.


A rule can potentially apply to a base form in a number of places; we define an anchored rule A = (R, i, j, b) to be the application of R to a span (i, j) in b.

a is only a valid anchoring if the substring of b between i and j matches the input of rule R. Given a set a of non-overlapping anchored rules for b, each entry of T (b) can be deterministically produced by rewriting each anchored rule's span (i, j) using the rule R. the task of predicting T (b) is equivalent to selecting a coherent subset a of anchored rules from the set of all possible anchored rules for this base form.


Furthermore, base forms and inflected forms separated by spaces, hyphens, or colons were discarded.

we discarded German verbpreposition compounds such as ablehnen 4 and Spanish reflexives such as lavarse.


Each iteration involves bootstrapping 10 different samples, and training 10 corresponding learners.

in total, the underlying prediction model is trained 1,900 times.


8 Figure 1 provides an intuitive explanation of these functions: cov 1 simply counts the number of selected data points with boolean feature j.

full coverage (||a j ||: the entire number of data points with the feature) is only achieved when all data points with the feature are selected.


If analyzers exist in such settings, they tend to be highly ambiguous, and annotated data for learning to disambiguate are also likely to be scarce or non-existent.

in our experiments with Russian, we compare two analyzers: a rapidly-developed guesser, which models regular inflectional paradigms but contains no lexicon or irregular forms, and a high-quality analyzer.


This marginalization produces the classical Chinese restaurant process representation (Teh, 2006).

when working with the morphology models we are proposing, we also need to marginalize the different latent forms (stems s and patterns p) that may have given rise to a given word w. we require that the inverse relation of GENERATE is available to compute the marginal base word distribution: Since our approach encodes morphology using FSTs, which are invertible, this poses no problem.


In order to model thematic roles, we use the insight that thematic fit correlates with human plausibility judgements (Padó et al., 2009;Vandekerckhove et al., 2009).

we can use datasets of human plausibility judgements to evaluate computational thematic fit estimates.


we use the maximum of the cosine similarities as the thematic fit score.


This could unfairly help the correlation between the cosines returned by the system and human judgements because the good role-fillers would all have the same cosine value, thus reducing the effect of the cosine ratings produced for the more distant (interesting) role-fillers.

we prohibit our system from returning any cosines of 1.0.


Typically, word usage statistics used to create a VSM form a sparse matrix with many columns, too unwieldy to be practical.

most models use some form of dimensionality reduction to compress the full matrix.


DM considers the conjunction as a semantically empty word and attaches an edge _and_c between the two verbs to mark the coordination.

this edge is more difficult to predict, because it is less informative, our baseline model relying on tokens, lemmas and POS.


Note, the authors supplemented corpora to include context that surrounds each citation; however, there is currently no freely-available, widely-used corpus which allows one to discern where citations appear within each report.


We wanted to use a corpus which was larger, provided the complete text of the original documents, and included meta-data such as author information.

we used the ACL Anthology (Radev et al., 2013) (the December 2013 release), which provides author and year information for each paper, and the corpus details are listed in Table 3.


This represents a large class-imbalance problem, which could make it difficult for the classifier to learn our task.


Note: the authors of PMTLM compared their system to LinkLDA for a different task (predicting research area) but did not compare to LinkLDA during their analysis of citation prediction performance.

it was not previously asserted that PMTLM would outperform Lin-kLDA.


sidering these parts of text for measuring the frequencies of character n-grams is not a good idea because signatures provide direct clues about the authorship of document and quotations do not reflect the author's writing style.

to clean up the CCAT collection, we preprocessed it to remove signatures and quotations from each document.


By contrast, CNN internally learns embedding of text regions (given the consituent words as input) useful for the intended task.

a large n such as 20 can be used especially with the bow-convolution layer, which turned out to be useful on topic classification.


Our method is inspired by the connection between syntactic linearization and syntactic parsing: both build a syntactic tree over a sentence, with the former performing word ordering in addition to derivation construction.

syntactic linearization can be treated as a generalized form of parsing, for which there is no input word order, and therefore extensions to parsing algorithms can be used to perform linearization.


(3) If l is the parent of k (Figure 6c), legal SHIFTs require all the words on the stack from l to the top to be reducable to k. (4) If l is a grand parent of k, all the words on the stack from l to the top will become descendants of l in the output (Figure 6e).

these words must be descendants of l in C, or the root of different subdependency trees.


However, the 128-beam system does not improve the performance significantly (48.2 vs 47.5), but runs twice slower.

we set the beam size to 64 in the remaining experiments.


As mentioned earlier, one problem of best-first is the lack of theoretical guarantee on time complexity.

a time constraint is used and default output can be constructed when no full output is found (White, 2004b;Zhang and Clark, 2011b).


During the decoding process, the best-first system compares spans of different sizes and expands   those that have higher scores.

the number of expanded spans do not have a fixed correlation with the size, and there can be fewer but better small spans expanded.


Moreover, a summary built from citing sentences that come for a pool of multiple citing papers is bound to lack coherence.

it is more appropriate to consider that the output of such a system is to extrinsically gauge a system's effectiveness in indexing information-rich citing sentences containing keywords that facilitate rapidly grasping a paper's important contributions, rather than be treated as a polished, readable summary for human consumption (Qazvinian et al., 2013).


As indicated in Section 1, the citation summary of a paper can be deemed a collective review of its contributions.

the main contributions of a paper are salient keywords, those keywords which are commonly used by its citers to refer to it and are statistically over-represented in the paper's citation summary w.r.t.


• The number of words already chosen to be part of the headline, which could be zero.

the local feature function f (x, i, s i-1 , y i ) will not only know about the whole text and the current token x i , whose category y i is to be assigned, but it will also hold information about the headline constructed so far.


As many previous studies report, an ideal headline must have from 8 to 10 tokens (Banko et al., 2000).

we include three binary features that correspond to the following conditions: (1) if the headline length so far is less than or equal to seven; (2) if the headline length so far is greater than or equal to 11; (3) if the token under analysis is assigned to the headline, which is a bias feature.


The approach we take in this paper leverages the fact that the speech signal in instructional videos is often closely related to the actions that the person is performing (which is not true in more general videos).

we first align the instructional steps to the speech signal using an HMM, and then refine this alignment by using a state of the art computer vision system.


These methods rely on necessarily limited collections of captioned images as sources of multimodal evidence, whereas we automatically enrich a very large corpus with images to induce general-purpose multimodal word representations, that could be used as input embeddings in systems specifically tuned to caption processing.

our work is complementary to this line of research.


Manually aligning each video segment with a sentence is tedious, especially for long videos.

it is desirable to automatically align video segments with their corresponding natural language sentences without direct supervision.


We can further reduce computation by decomposing the forward-backward recursions to the co-occurrence features and alignment path features: The potential due to alignment path features (Ψ ap ) does not depend on the subset of blobs, and only depends on the current and previous alignment states h[n] = m and h[n − 1] = m .

on the other hand, the co-occurrence potential Ψ co for a given set of blobs Y depends only on the sentence that it is being aligned to, and does not depend on the video chunk index n. we can decompose the forward recursion as: where δ(m) = Y ∈P Ψ co (X im , Y ).


Finally, we update the weight vector w: for n = 1 to ni do 9: for each (m , n − 1) ∈ Predecessors(m, n) do 10: During the full decoding of (ĥ i ,ŷ i ), we have no information regarding how many video chunks to assign to each sentence.

the full decoding is unlikely to predict the correct video sequence, no matter how many training iterations performed.


However, including these features did not provide any noticeable gain, while significantly increasing the computation time, as the number of features increased drastically.

we did not include these features in our final experiments.


We used the following alignment path features: • Jump Size: Since we allow monotonic jumps only, the jump sizes can be either zero or one.

we added two features for these two jump sizes.


E.g., younger, urban and minority populations tend to be overrepresented on Twitter (Mislove et al., 2011;Lenhart and Fox, 2009), and Latino users tend to be underrepresented on Facebook (Watkins, 2009).

it is incorrect to assume that the demographics of Twitter users from a county match those of all people from a county.


Note that both imp-greedy and logistic use the same amount of labeled data, though in different ways: logistic performs standard supervised classification; imp-greedy uses the labeled data to perform constraint selection for label regularization.

to summarize our answer to RQ2, we find that imp-greedy provides a robust method to select constraints in the presence of noise.


As shown by the graph, we obtain high precision (about .9) at recall 0.5.

applying our method on a large dataset yields high quality sentence alignments that would benefit data-driven learning in text simplification.


Unlike written articles, broadcast news does not indicate where one topic starts and another ends.

we experimented with different size contexts by including the utterance containing the name mention and up to eight utterances before and after so long as they occurred within 150 seconds of the start of the utterance containing the name mention.


Intuitively, pairwise weights P P R(s → e) represent relationships between nodes in the graph: the higher the weight is, the more relevant endpoint e is for the source s. Candidate nodes in the graph have different quality, measured by their initial similarity iSim.

coherence of the node e to the graph G due to the presence of node s is given by coh s (e) = P P R(s → e) • iSim(s), where relevance e for s is weighted by the iSim(s), which is the similarity between entity e and candidate s. We experiment with a MaxEnt-trained local score and the Freebase popularity as the iSim in Section 5.


the random walker, started from any node in this component, will land at any other node in this component with some positive likelihood.


However, (c1) and (c2) will eliminate contribution from some of the candidates: Constraint (c1) does not allow Lincoln Lincolnshire, 0.3 and Boston Lincolnshire, 0.3 to contribute, because they compete for the same entity mention as candidate Lincolnshire, 0.4; constraint (c2) will allow only one contribution from either Lincoln United F.C., 0.5 or Boston United F.C., 0.5 whichever is bigger, since they compete for the same entity mention United F.C..

set CON T R e(m,c) for entity mention m = Lincolnshire and candidate c = Lincolnshire, 0.4, will contain only two contributors: candidate Devon White (footballer), 0.5, for entity mention Devon White, and exactly one of the candidates for entity mention United F.C.


Our goal is to find the best candidate for every entity given a candidate's coherence and its initial similar-ity to the entity.

to combine the coherence score coh(e) with iSim(e), we weight the latter with an average value of P P R weights used in coherence computation (3) across all nodes in the document graph G(V, E): the final score for node e is a linear combination score(e) = coh(e) + P P R avg • iSim(e) (5) If the document graph has no edges then P P R avg is zero and for any node e its coherence coh(e) is zero as well.


Training time is equivalent to choosing γ = 1, and larger values of α result in decreased BLEU, while smaller values result in significantly increased normalizer risk.

we see that we can achieve smaller normalizer variance and an order-of-magnitude decrease in training time with a loss of only 0.1 BLEU.


Adjacent ECs before a certain token usually have different head words, which means they are attached to different nodes (head words) in a parse tree.

it is possible to define positions using "head word, following word" pairs.


At the same time, inventories change on a daily basis.

the collected information is typically sparse and often outdated.


Typically, both text categorization and NER approaches are applied to genres with exclusively text-based content (newswires, scientific publications, blogs and other social media texts).

the feature space of NER and text categorization involves purely textual features: word attributes and characteristics, their contexts and frequencies.


In an MSCFG, the elementary structures are rewrite rules containing not a source and target, but an arbitrary number M of strings where X is the head of the rule and η m is a string of terminal and non-terminal symbols.

4 In this paper, for notational convenience, we will use a specialized version of Equation 3 in which we define a single γ as the source side string, and α 1 , ...α N as an arbitrary number N of target side strings: at each derivation step, one non-terminal in γ is chosen and all the nonterminals with same indices in α 1 , ..., α N will be rewritten using a single rule.


4 We will also make the restriction that indices are linear and non-deleting, indicating that each non-terminal index present in any of the strings will appear exactly once in all of the strings.

mSCFGs can also be thought of as a subset of the "generalized multi-text grammars" of melamed et al.


As our main motivation for multi-target translation is to use T2 to help translation of T1, we can assume that the final of these three probabilities, which only concerns T2, is of less use.

we propose two ways for pruning the rule table based on the former two.


However, since the training sets for some of the benchmark tasks are small, tuning the window size will not be consistent over different tuning sets.

we decided to select the same window size for all words in a task and tune this parameter on the whole tuning set instead.


This is because the available training sets are small and using 20% of these samples as the development set means that the development set only contains a small number of samples.

the selected development sets are not proper representatives of the test sets and the tuning process results in overfitting the parameters (window sizes) to the development sets, with low generalization accuracy.


However, per-task tuning is relatively stable and performs better on the test sets.

we have selected this method of tuning in all our experiments.


It considerably outperformed (1) the random baseline of 22.4% and (2) the most-frequent-value baseline of 28.1%.

our assumption of dependencies among features was confirmed.


For example, phd in the interpretation "job for phd in chemistry" is incorrectly tagged as a past participle verb.


Both HMM-GMM and HMM-DNN systems remain difficult to build, and nearly impossible to efficiently optimize for downstream SLU tasks.

sLU researchers typically operate on an n-best list of possible transcriptions and treat the LVCsR system as a black box.


Because our model assumes the character labels for each timestep are conditionally independent, C * is simply the most probable character at each timestep in our DBRNN output.

this decoding procedure is very fast to compute, requiring only time O(T |ζ|).


Similarly, "Bieber memes" involve modified pictures of Bieber.

we hypothesize that, by extracting visual features, it is of crucial importance to capture the entities, objects, and styles as visual words in these inter-related meme images.


See Gritzmann and Sturmfels (1992) Each vertex h i corresponds to a single index vector i, which itself corresponds to a single set of selected hypotheses.

the number of distinct sets of hypotheses that can be drawn from the S K-best lists in bounded above by O(min(K S , S D−1 K 2(D−1) )).


Each feasible pairwise ranking of pairs of hypotheses corresponds to an edge in the Minkowski sum polytope.

in low dimension ranking methods also benefit from this inherent regularisation.


structures from which the generation naturally starts often do not contain any functional nodes, while surface-syntactic structures or a chain of tokens in a linearized tree contain all of them.

data-driven linguistic generation needs to be able to cope with the projection between non-isomorphic structures that differ in their topology and number of nodes.


There is already a body of work available on statistical linearization.

these tasks were not in the focus of our work.


The linearizer also suffers from a small size of the training set.

while the small Spanish training corpus leads to 0.754 BLEU and 0.762 BLEU for the development and test sets respectively, for English, we achieve 0.91 BLEU, which is a very competitive outcome compared to other English linearizers (Song et al., 2014).


6 Such messages would be marked as 'present' in our annotation scheme.

one might consider our present class to encompass both a present and "non-temporal" class.


Just as two words can only be antonyms if they are in the same semantic field, two arguments can only be contradictory if they are about the same FACET.

we instruct annotators to give a score of 3 to opposing arguments on the same FACET.


The goal is to infer spatial knowledge as gathered by humans when reading text.

annotators were encouraged to use commonsense and world knowledge.


We use induction to give a proof that S[i][j] = max T e i ∈T w i after running our algorithm, where T is a set of tree nodes that satisfies T ∈ T i and e i ∈T l i = j. , then the set is contained in T lo(i) , and its size is j.

the maximum weight equals S[lo(i)][j] (Alg.1 line 9).


To approximate this objective we use a weighted average of all contexts of u, where contexts are weighted according to their similarity to the given context: where C cu = C u ∪ c (u's corpus contexts plus the given context) and Z is a normalization factor.

is the average fitness of v within the contexts of u, biased to those similar to c. We consider this a contextsensitive similarity score, indicative of the likelihood of v to be a paraphrase of u in context c. 2 we name our in-context representation for a word instance as its paraphrase vector.


(2013a) follow a supervised approach, training on the LS07 gold standard with 10-fold cross validation, as well as incorporate features from WordNet.

they cannot be directly compared with unsupervised models, such as our own.


We used w = 5 and found CBOW to yield better results for our task.

the terms extracted from a corpus by the structural patterns are automatically clustered, and these clusters are used as an input to the ranking algorithm.


We found that a dimension size of 600 for the vectors yielded clusters with a maximum F1 score of 57%.

we were able to fix the parameters for our clustering task.


It could be argued that the gold standard cluster "deceptive, false, fraudulent, misleading" represents different degrees of "trickery," whereas the automatic cluster "false, misleading, unreliable, wrong" represent different degrees of "wrongness."

although both clusters contain different adjectives, they group adjectives that are on the same scale of a different meaning.


Thus, although both clusters contain different adjectives, they group adjectives that are on the same scale of a different meaning.

to evaluate the quality of the automatic clustering, we sampled 50 clusters containing three or more adjectives (corresponding to a total of 190 adjectives) from all the generated clusters and obtained annotations using Amazon Mechanical Turk (AMT), a crowdsourcing platform that has been shown useful for a number of NLP tasks (Snow et al., 2008).


We believe that this is due to the fact that the adjectives in the dataset used in the previous experiment originate from WordNet and contain many words (e.g., handsome, crazy, spicy), which are less likely to be found in scientific abstracts.

the context vectors learnt for these words are possibly less accurate compromising the clustering quality.


However, we also observe that with domainspecific words, context is important for establishing a ranking between words that is based on semantic intensity.

rather than clustering adjectives or adverbs in isolation, a joint with the clustering of nouns or verbs with which they occur is a possible direction of research.


Although we can use "bronze standard" annotations for learning, evaluating against a bronze standard is not directly useful.

we annotated our test set (1152 utterances) by hand.


The latter in general are literal and mechanical readings of the visual scenes, while the former reflect a rich spectrum of natural language utterances in everyday narratives, including figurative, pragmatic, and conversational language, e.g., "can you spot the butterfly" (Figure 1).

this dataset offers unique opportunities for grounding figurative and metaphoric expressions using visual context.


Note that while this automatic evaluation is closer to the real-world scenario, it is still not perfect as the new KB snapshot is still incomplete.

we also perform human evaluation on a small dataset to verify the effectiveness of our approach.


However, there is some ambiguity in the opposite direction; for example, the phoneme [b] can be expressed with either 'b' or 'v'.

it is not unusual for native Spanish speakers to make spelling mistakes.


Since their focus is on the task of inducing text-to-speech rules, they also incorporate letter context into this definition.

a system that is completely explained by a set of rules has a perplexity of 1.


The deep learning approach, especially Stacked Denoising Auto-encoders (SDA), has been shown highly beneficial for extracting domain-independent knowledge (Glorot et al., 2011).

we use SDA to construct the corpus-based sentiment classifier.


(2014) that are performing multiview learning and could be considered here as baselines: however it is not straightforward to use those systems to handle the variety of data that we are using.

we directly compare our method to the Glove and the SkipGram model of Word2Vec as the performance of those systems is considered state of the art.


Given that the amount of contextual information gathered for a concept can be small, the resulting word-based vector can be sparse and as a consequence prone to noise, especially in the case of less frequent concepts.

we put forward a method that tackles the issue, providing rich semantically-aware representations.


This corresponds to a system that correctly detects 48 per cent of all instances of the spoken query terms, while producing at most ten false alarms for every missed detection (Fiscus et al., 2007).

if users are willing to tolerate low precision, moderate levels of recall are possible.


Of particular note, however, is that within the bounds there are a potentially large number of terms that can be used to describe a region of speech.

the larger the number of terms present, the larger the amount of redundancy in the segment of speech each term describes.


Under pure clustering, Ua was generally the worst performer.

query refinement using the temporal extent of pseudo-terms is a good idea.


Another important property of borrowing is that in adaptation of borrowed items, changes in words are systematic, knowledge of morphological and phonological patterns in a language can be used to predict how borrowings will be realized in that language, without having to list them all.

modeling of borrowing is a task wellsuited for computational approaches.


The posteriors of more advanced word alignment models (such as fertility-based models) do not correspond to alignments, and furthermore, are already estimated with approximation techniques.

even if we somehow adapt ABA's product-of-marginals heuristic to such models, we run the risk of estimating highly inaccurate posteriors (specifically, zero-valued posteriors).


• Slot-based semantic knowledge graph is built as • Word-based lexical knowledge graph is built as With two knowledge graphs, we build the edges between slots and slot-fillers to integrate them as shown in Figure 3.

the combined graph can be formulated as and E ws correspond to slot-to-slot relations, wordto-word relations, and word-to-slot relations respectively (Chen and Metze, 2012; Chen and Metze, 2013).


To adequately and robustly deal with paraphrases, a large-scale knowledge base containing words and phrases having approximately the same meaning is indispensable.

the task of automatically creating such large-scale paraphrase lexicons has been drawing the attention of many researchers (see Section 2 for details).


Previous work (Chan et al., 2011;Fujita et al., 2012;Ganitkevitch et al., 2013) has proved that the information obtained from monolingual data can be used for assessing bilingually originated paraphrases.

pairs that have low contextual similarity are also filtered out.


Pattern matching alone can generate pairs that are not suitable as paraphrases in any context.

we assess the reliability of each pair by calculating contextual similarity between two phrases in the same manner as cleaning S Seed : a pair of phrases is eliminated, if the phrases are used in completely dissimilar contexts.


(11) a. development of tourism ⇔ developing tourism b. development of X ⇔ developing X On the other hand, LEXVAR also learns patterns from seed paraphrase pairs that IDENT ignores, e.g., (6) and (9a).

a wider range of seed paraphrases were involved in learning patterns and more patterns were acquired.


H L , H R are respectively H's left dependents and right dependents, plus EOC (End-Of-Children), a special token to inform that there are no more dependents to generate.

p (d(ROOT )) is the probability of generating the entire dependency structure d. Le and Zuidema's ∞-order generative model is defined as Eisner's model in which the context C ∞ (D) to generate D contains all of D's generated siblings, its ancestors and their siblings.


They cannot be in the stack or buffer since the stack and buffer always remain in order; they cannot be in registers by the precondition R1 / for Right-Arc transitions.


There cannot be any overlap between register arcs where the corresponding tokens were not in the registers simultaneously: the Clear transition updates the book-keeping constant last to be the rightmost vertex associated with  the registers being cleared, and subsequent actions cannot introduce crossed arcs to the last token or to its left (by the β 1 > last and σ 1 > last preconditions on storing and register-stack arcs, respectively).

each set of tokens that were in registers simultaneously defines a crossing interval.


The ordering h < g < m is not possible, since if (g, h) had been added through a left-arc, then h would have been reduced, and if (g, h) and (h, m) were both added through register-stack arcs, then one of them would have violated the (R close , σ 1 ) / ∈ A or the (σ 1 , R f ar ) / ∈ A precondition.

similar reasoning can rule out m < g < h. Condition (b) of Definition 3 is also satisfied.


Taking the partial derivative of the objective in equation 1 with respect to vector v ij and setting to zero gives the following solution: where N ij denotes the set of neighbors of ij.

the MAP sense-specific vector is an α-weighted combination of its sense-agnostic vector and the β rweighted sense-specific vectors in its ontological neighborhood.


Both our approaches are sensitive to the structure of the ontology.

an important consideration is the relations we use and the weights we associate with them.


(2013) do model senses from an ontology by performing random-walks on the Wordnet graph, however their approach does not take distributional information from VSMs into account.

to the best of our knowledge, our work presents the first attempt at producing sense grounded VSMs that are symbolically tied to lexical ontologies.


They complete tasks without any oversight.

translations produced via crowdousrcing may be low quality.


Table 2 shows the results of Pearson correlations for different value of k. As k increases, our rankings fit the gold ranking better.

we can decide whether to continue to hire a worker in a very short time after analyzing the first k sentences (k ≤ 20) provided by each worker.


Although the unit cost for each reference is much higher than the unit cost for each non-professional translation, the cost associated with non-professional translations can dominate the total cost since the large amount of data need to be collected.

we focus on reducing cost associated with nonprofessional translations.


The similarity relationships among words cannot be utilized to imporve the coherence of topic modeling.

noise words such as will, year, used which cannot effectively represent a topic, show up due to their high frequency.


However, they improperly enforce words labeled as similar to have similar probabilities in all topics, which violates the fact that whether two words are similar depend on which topic they appear in.

the topics extracted by these two methods are unsatisfactory.


The NER task is not always as intuitive as we would like, and organizations tend to highlight these difficulties.

far, we have restricted ourselves to a setting without access to linguistic resources, but for some  languages, such as English, rich resources exist and can be very useful.


In contrast, supervised models must balance metadata (i.e., response variable) prediction against capturing word meaning.

sLDA does slightly worse with respect to topic interpretability.


(2014) learned from n-ary relations as well, yet their formulation only allows relations between entities, not relations between relations.

their approach cannot represent nested events, let alone extracting them.


As proven in (Klavans and Kan, 1998), nouns, verbs, and adjectives were indeed beneficial in document analysis.

we focus on choosing bigrams containing these words.


For DBpedia, since it only has feature values for the bigrams containing name entities, it will only assign weights for those bigrams.

only considering DBpedia features means that the ILP decoder would prefer to choose bigrams that are name entities with positive weights.


We only include the discourse connectives that appear in both explicit and implicit discourse connectives in the PDTB to make the comparison and classification possible.

we only analyze 69 out of 134 connectives for the purpose of classification.


the length of the TOPIC feature vector was 72.


A noun phrase is also a sequence of words: where each w j is the j th word in the noun phrase.

each segment is a series of words: where l is the length of semantic segment i.


Recall that consecutive words with the same label in a noun phrase are treated as a segment.

if a segment s CRF identified by the CRF has the same boundary and the same label as a segment s Human in the noun phrase containing s CRF , this segment s CRF will be counted as a correct segment.


The results reveal that in case of applying trigram LMs F 1 degrades about 9% absolute compared to parsing written text (reference), yielding 78.36% in F 1 , even though the WER is rather low with a value of 7.1%.

the trigram seems to "destroy" semantically meaningful sequences while restoring sequences that contain no meaning.


This is due to the fact that the number of FPs can range from zero to the size of negative labels, and the number of FNs can range from zero to the number of positive labels.

the loss term can take O(N 2 L 2 ) different values which can Algorithm 3 Finding Y * : Local Search 1: procedure OPT-LOSSLAG(Λ, Y) 2: Initialise (f p, f n) on the grid 5: for ((f p , f n ) ∈ Neigbours(f p, f n) do 7: : if loss (f p,f n) > loss (f p ,f n ) then 10: else 12: 13: 14: return { Y corresponding to (f p, f n) } be represented on a two-dimensional grid.


Exhaustive search in the space of all possible grid points is not efficient as soon as the grid becomes large.

we have to adapt the techniques proposed in previous work (Ranjbar et al., 2012;Joachims, 2005).


The motivation for convolution is that natural language consists of long sequences in which many short subsequences contribute in a stable way to the structure and meaning of the long sequence regardless of the position of the subsequence within the long sequence.

it is advantageous to learn convolutional filters that detect a particular feature regardless of position.


It extracts k dy top values from each dimension after the first layer of averaging and k top = 4 top values after the top layer of averaging.

we set k dy depends on the length of S i .


The reason is that sentence representations are computed by k-max pooling from level ln.

we can roughly view the sentence-level feature matrix F s as a subset of F ln .


Note that one query can belong to multiple domains.

a set of binary classifiers are built, one for each domain, to perform the classification.


For Czech, the percentage includes the testing and held-out sets.

the method of Durrett & DeNero and our reranker have access to less training data than in the experiment of Section 4.4.


Our strategy is to choose a set of "interesting" substrings W. For each w ∈ W, define a feature function "How many times does w appear as a substring of v?"

f (v) is simply a vector of counts (nonnegative integers), indexed by the substrings in W. A natural choice of W is the set of all n-grams for fixed n. In this case, Q turns out to be equivalent to the family of n-gram language models.


For each arc or final state a in A, we can define a feature function "How many times is a used when A accepts v?"

f (v) is again a vector of non-negative counts.


In this general setting, ENCODE(θ) just returns a weighted version of A where each arc or final state a has weight exp θ a in the (+, ×) semiring.

this WFSA accepts each v with weight exp(θ • f (v)).


These strings are favored by at least one of the current messages to V (Dreyer and Eisner, 2009;Dreyer and Eisner, 2011;Cotterell et al., 2015).

simplifying one of V 's incoming messages considers all of them, as in EP.


To compare apples to apples, this experiment fixed the set of µ F →V messages for each variable.

we held the set of beliefs fixed, but measured the size and accuracy of different approximations to these beliefs by varying λ.


We have observed that nbest lists produced by our joint model contain the correct transliteration more often than the baseline models.

we follow the joint generation with a reranking step, in order to boost the top-1 accuracy.


In fact, news writers often use slightly different transliterations of the same name, which makes the model's task more difficult.

we rerank the model outputs using a target-language lexicon, which is a list of words together with their frequencies collected from a raw corpus.


Taking these findings into account, we could also mark as prosodic boundaries all syllables which are signalled by at least two different cues, regardless of the value of these cues.

by employing the conjunction of cues we can give a higher weight to a group of cues which, by appearing together, mark more reliably the presence of a boundary, in the hope that it would increase recall without decreasing too much the precision.


While this corpus is indeed larger, it has the disadvantage of not being prosodically annotated.

we investigated only the cases when automatically determined prosodic boundaries are employed.


Furthermore, maxRecall, which has very low precisions, seems to behave similar to, or below the baseline, for both datasets.

it appears that a relatively high precision for the prosodic boundaries is needed to obtain improvements in word segmentation and, once this condition is fulfilled, any increase in recall would increase the gain over the baseline.


In contrast to the above studies, which have concentrated on text translation, this paper focuses on spoken language translation within a bilingual human-human dialog system.

our main goal is not to predict the correct morphological form of every word, but to prevent communication errors resulting from the mishandling of morphology.


This is particularly risky since the daily amount of specific data is relatively small (about 3k words).

we chose to add three times the daily data to our existing in-domain development set.


procedure would ordinarily be too expensive since there are O(k 2 ) pairs per sentence, where both k and the number of sentences can be in the thousands, so billions of training examples would be produced per iteration.

hopkins and May (2011) use subsampling to consider a small percentage of all pairs per sentence.


However, with unary rules and more-than-binary branchings, constituency parsing presents challenges not found in dependency parsing that must be addressed before applying DP.

we first present an odd-even shift-reduce constituency parser which always finishes in same number of steps, eliminating the complicated asynchronicity issue in previous work (Zhu et al., 2013;Wang and Xue, 2014), and then develop dynamic programming on top of that.


Second, to handle orthographic variation, we provide an interface that allows individuals familiar with relevant languages to guide the language model with targeted orthographic information.

our system handles inconsistent spelling, punctuation, and diacritic usage, as well as now-obsolete shorthand conventions used by printers.


We learn this group of multinomial parameters, θ for each language, in an unsupervised fashion, and in doing so, adapt to the proportions of languages found in the particular input document.

using our parameterization, the probability of transitioning from language to , given previous character e, is:   Finally, because our code-switching LM uses multiple separate language-specific n-gram models P CHAR , we are able to maintain a distinct set of valid characters for each language.


In biomedical domain for example, PubMed -the largest repository of biomedical literature -contains more than 24 million articles.

there is a need for concise presentation of important findings in the scientific articles being published.


Combined reformulation: Due to the narrative structure of citances and their relative long length, using all citance terms for expansion is likely to cause query drift.

we first reduce the citance using one of previously described reduction approaches and then apply query expansion.


However, the dense reference features may be lost among the many sparse response features.


On the other hand, articles and comments are from intrinsically different genres of text: articles emphasize facts and are written in a professional style; comments reflect opinions in a less formal way.

it could be difficult to recognize the connections between articles and comments.


We apply a pairwise preference-based regularizing constraint (Joachims, 2002) to incorporate a bias toward editor's picks: .Ỹ c is a vector of same size asX c with each element as 1.

the objective function to minimize is: Graph-Based Regularization.


We normalizeR byQ The interplay between the two types of data is encoded in the following regularizing constraint: Full Objective Function.

the full objective function consists of the three parts discussed above: Furthermore, using the following notation, we can show a closed form solution to Equation 4 as follows: Basic Features Social Features -num of words -avg/sum frequency of -absolute/relative position words appearing in comment -overlaps with headline -avg/sum frequency of -avg/sum TF-IDF scores dependency relations -num of NEs appearing in comment -TF/TF-IDF simi with comments -contains URL -JS/KL divergence (div) with article -user rating (pos/neg) -JS/KL div with comments Sentiment Features -num /proportion of positive/negative/neutral words (MPQA (Wilson et al., 2005), General Inquirer (Stone et al., 1966)) -num /proportion of sentiment words  Now we present an optimization framework for timeline generation.


It is based on the intuition that there exists a canonical order of how entities occur in the text.

we can model text coherence by measuring how mentions of various entities are distributed within the text.


For a fair comparison, we also evaluate our model on a filtered subset of documents with an average length of 31.8 sentences.

our dataset contains 72 documents and 72 × 20 = 1440 permutations, among which the shortest one contains 25 sentences.


This observation is quite intuitive, because the longer a document is, the higher the chance is that, after being shuffled, adjacent sentences in the resulting permutation would be completely irrelevant to each other.

for longer documents, it is much easier for the model to distinguish a permutation from its source document.


We postulate that it is primarily because a document with fewer sentences tends to shift to another subtopic immediately without elaborating on the previous one, and strict entity matching would find it difficult to establish coherent transitions between them.

the contribution from semantic relatedness tends to dominate the overall performance.


In practice, however, some candidate antecedents are implausible given the context.

to identify such candidate antecedents, we employ a simple heuristic, which considers a candidate antecedent implausible if its event subtype is different from that of e. we model P (c|k) as follows: if c is implausible, we set P (c|k) to 0 and distribute the probability mass uniformly over all and only the plausible candidate antecedents.


We only consider documents that have either been tagged by four or more users with a predetermined degree of similarity and verified by one or more author (150 documents), or documents tagged by the authors in committee (250 documents).


In fact, half of the events extracted by information extraction (IE) systems lack time arguments (Ji et al., 2009).

if an AMR parse includes no time information, we use the document creation time as an additional collaborator for mention in question.


The scoring function needs to reflect the highorder interactions between the predicate, argument, their syntactic path and the corresponding role label.

we parametrize the scoring function as a four-way tensor.


In contrast, the scoring function in SRL involves higher-order interactions between the path, argument, predicate and their associated role label.

we parametrized the scoring function with a four-way low-rank tensor.


In contrast, retrieval systems often use bag-of-word representations, stopword filtering, and stemming techniques during document scoring, and queries are rarely fluent, grammatical natural language queries (Downey et al., 2008).

most of a translation's structural information is lost during retrieval, and lexical choices may not be optimal for the retrieval task.


However, most previous studies only focused on using triggers to create more patterns (e.g., ).

the critical problem is how to make the most of triggers in biographical fact extraction?


Their relative performance, though higher than the baselines, varied between the datasets.

for conciseness, we present results only for expanding both positives and negatives.


As seen in Figure 1, the word embedding of the word table is more closer to the centroid C 2 as compared to the centroids C 1 and C 3 .

the MFS of the word table is chosen as S 2 {a piece of furniture having a smooth flat top that is usually supported by one or more vertical legs}.


We cannot use an off-the-shelf syntax parser to create a tree for the chain because the chain may not necessarily be a coherent English statement.

we build two Directed Acyclic Graph (DAG) structures by heuristics.


Aligning the indices can result in zero similarity even though the two pieces of texts are related.

we propose to align different indices of x and y together to increase the similarity value.


In this case, instead of introducing the relatedness between nonidentical terms, it will also introduce noise.

we also consider an alignment mechanism that we implement greedily via a maximum matching mechanism: (3) We choose j as argmax j φ(a i , b j ) and substitute the similarity φ(a i , b j ) between terms a i and b j into the final similarity between x and y.


It has been shown that ESA outperforms other representations for dataless classification (Chang et al., 2008;Song and Roth, 2014).

we chose ESA as our  Figure 2: Boxplot of similarity scores for "rec.autos vs. sci.electronics" (easy, left) and "rec.autos vs. rec.motorcycles" (difficult, right).


The gradient, as in all energy-based models, takes the form of the difference between two expectations (LeCun et al., 2006).

we propose a multi-task objective that jointly predicts the next word w and its morphological tag t given a history h. we are interested in a joint probability distribution defined as where f t is a hand-crafted feature vector for a morphological tag t and S is an additional weight matrix.


These two models make different independence and conditioning assumptions; however, both models discard word order information in how they account for context.

embeddings built using these models have been shown to capture semantic information between words, and pre-training using these models has been shown to lead to major improvements in many tasks (Collobert et al., 2011).


For the POS induction task, we specifically need embeddings that capture syntactic similarities.

we experiment with two types of embeddings that are known for such properties: • Skip-gram embeddings (Mikolov et al., 2013) are based on a log bilinear model that predicts an unordered set of context words given a target word.


2 Recently, to create the Sentiment Treebank (Socher et al., 2013), researchers crowdsourced annotations of movie review data and then overlaid the annotations onto syntax trees.

the targets are not limited to aspects of products/services.


However, annotators were asked to annotate small and then increasingly larger segments of the sentence.

the annotations are mixed in the degree to which context was considered when making the judgements.


This is captured by an objective speech event annotation (not shown) whose target span includes the insult event, and whose source is the writer (w).

the complete interpretation of this negative attitude is, according to the writer, the Imam is negative to-ward the insult event.


Note that the span covering this event is the target span of the attitude in Ex(1) -the private state of Ex(2) is nested in the private state of Ex(1).

the complete interpretation of the negative attitude in Ex 2is: according to the writer, the Imam is negative toward Rushdie insulting the Prophet.


Here the three organizations are against the fight being used as a pretext for civil rights violations.

"be", "pretext", and "violation" are eTargets, but "fight" and "terrorism" are not.


In contrast, memory bandwidth is extremely high on modern GPUs.

if it were possible to order the training vectors x i and y i in GPU memory such that neighborhood matrices G(x) and H(x) were composed of contiguous blocks of training vectors, extraction on the GPU could be made very efficient.


Unfortunately, with the current definition of the neighborhood function, g, such an ordering does not exist.

we define a new GPU-friendly neighborhood function,g, for which an ordering that permits contiguous extraction does exist.


A Although we examine the entire conversation, words that are too far from the source utterance (for example, 50 utterances apart) will rarely have much semantic impact to the current topic.

it is necessary to keep the size of the conversation reasonably small, and we restrict it to be at most 8 utterances.


This could occur either when prepositions are dropped from the relation phrase or when there is an ambiguity in the boundary of the relation phrase.

to evaluate our multilingual relation extraction framework, we obtained annotations from professional linguists for three typologically different languages: French, Hindi, and Russian.


2 Our model jointly learns feature embeddings and a tensor-based classifier which relies on the outer product between features embeddings and word embeddings.

the number of parameters are dramatically reduced since features are only represented as low-dimensional embeddings, which alleviates problems with overfitting.


Using the same features as in the baseline, we introduce a two-stage CRF model motivated by our observation that many repetitions are omitted from the standard transcriptions.

while 62% of disfluencies in CAREFUL SCOTUS are repetitions, only 22% of all disfluencies in ANNOTATED OYEZ are repetitions.


As we will show in Section 4.1, dictionary look-up from lexicons is not sufficient.

the first part of our research aims to create an effective medication detector for these informal veterinary texts.


A notable example is WordNet (Fellbaum, 1998), which encodes a taxonomy of concepts and semantic relations between them.

wordNet has enabled a wide variety of NLP techniques such as word Sense Disambiguation (Agirre et al., 2014), information retrieval (Varelas et al., 2005), semantic similarity (Pedersen et al., 2004;Bär et al., 2013), and sentiment analysis (Baccianella et al., 2010).


First, Wiktionary entries are defined in terms of lemmas, rather than senses.

directly ontologizing the resource or integrating its semantic relations requires disambiguating each relation's lemmas, which is not always possible due to the limited context.


Directly comparing glosses with string similarity measures omits the important detail that certain lemmas can be highly-specific and most strongly indicate that two glosses refer to the same concept.

prior to comparison, the lemmas occurring in all glosses are assigned a weight −log 1 f (w) , where f (w) denotes the number of glosses in which lemma w appeared.


This example demonstrates the importance of estimating good translation probabilities in order to improve the clas-sification of the source text.

we propose a probabilistic model that uses latent document topics to help improve the translation probabilities for a source document.


In the humanitarian crisis framing vs. regional security framing example above, a regional security framing does not negatively valence human suffering; rather, it shifts the emphasis to make other concerns more apparent.

while we can draw on subjectivity, bias, and sentiment detection, identifying framing requires features and techniques that go beyond any one of these related concepts.


While the framing literature provides some guidance (Entman, 1993;Chong and Druckman, 2007), little work has explored how exactly non-elites go about identifying framing (Chong and Druckman, 2007).

we conducted a pilot investigating to explore laypersons' understandings and identification of framing.


In ostensibly "straight" news, though, framing may be harder to identify.

this context would benefit more from a classifier that could automatically draw attention to frame-invoking language.


Few Words Annotated -When very few words in a document are annotated, we suspect the annotator may have been completing the task as quickly as possible and paying little attention.

we required that each annotator's work include enough annotated words to total more than 5% of the document's length.


However, accounts from our pilot study participants made us less likely to believe that other single words, such as "an," "of," or "that," instantiated framing.

we require that no more than 3 annotated passages consistent entirely of such stop words1.


Based on our pilot study, we would expect annotators to agree to some degree, but we would not want such agreement to arise because annotators were collaborating.

we also exclude these annotations where we suspect the possibility of collaboration.


Furthermore, many of our pilot study participants pointed out that a given word might be seen as related to framing because of the other words near which it appears.

each of these features includes a contextual window of up to two words before and after the word being classified (Recasens et al., 2013).


However, we have little means of knowing a priori which of these features will be most important or even necessary.

in the interest of developing the most perspicacious model, we test several feature subsets, as noted in Table 1.


The author also suggests that they are useful for acceptability and long-term retention of original expressions, decrypting their meanings, indicating informality, and breaking the ice be-tween an audience and a speaker.

these devices are commonly used in original metaphorical and metonymical compounds.


The normalized posterior translation probability p Θ (E|F ) from source sentence F to target sentence E approximates a maximum entropy model normalized on sentence level: The denominator of this probability does not depend on the output sentence.

the arg max of Equation 8 is equal to the arg max of the translation score in Equation 1.


Due to the renormalization component, this results in a total of 6.08M features that are updated with GT using the same data.

it is less time and space efficient than the other algorithms.


Furthermore, if two points q and p are within a certain radius r, i.e., q − p ≤ r, then frequently |q (i) − p (i) | ≤ r, where x (i) denotes the i-th element of vector x.

the neighbor query can be approximated as a check whether the distance between p and q on each dimension is less than r, 1.


Further, we see that most of that decrease is recovered with richer features.

we conclude that it is empirically reasonable to model these phenomena together.


We have motivated the task of supersense tagging in part as a coarse form of word sense disambiguation.

it is worth investigating how well the learned model manages to choose the correct supersense for nouns and verbs that are ambiguous in the data.


To the best of their abilities, they try to convince the reader to buy or not to buy a particular product, and, according to Grice's Maxims (Grice, 1975), they do it in the clearest and most effective way possible.

if an author has in general a positive opinion of a product, the probability of a negative sentence appearing in a review is lower than that of a positive sentence, and even if a negative sentence is introduced, it is likely to appear together with a concession or a contrast marker, such as although or but, or be modified by a hedging expression, such as might, only, could be, which mitigate the negative effect on the reader.


Thus, if an author has in general a positive opinion of a product, the probability of a negative sentence appearing in a review is lower than that of a positive sentence, and even if a negative sentence is introduced, it is likely to appear together with a concession or a contrast marker, such as although or but, or be modified by a hedging expression, such as might, only, could be, which mitigate the negative effect on the reader.

the author makes us understand that his primary opinion of the product is still positive, and uses the discourse relation of contrast to present an opposite opinion.


To set a baseline, we use a state-of-art lexical classifier -Stanford Sentiment Analysis Classifier from Stanford CoreNLP toolkit 6to determine the lexical polarity of each individual sentence.

the lexical classifier considers only lexical features available in a particular sentence without looking at neighboring sentences or discourse cues.


Precision, recall, F1 and accuracy scores for the top-down approach Also, because 3-star reviews contain more negative sentences than positive ones, all of them are lumped into the negative class.

the recall for the positive class is substantially low than for the negative class.


On their logic, with greater amounts of contextual information, the predictability of linguistic material based on context, I(X i |C i , L i ), must go up.

they predicted that H(X i |L i ) should also increase, so as to maintain a constant total amount of information.


Readers fixate longer on less predictable lexemes than on more predictable lexemes, but the predictability of a lexeme depends on the preceding context.

it is common for psycholinguistic models to include a measure of n-gram predictability for each fixated word conditioned on its context, but unless probabilities for words between fixations are also included, the probabilities used in this calculation are   Table 1, the standard bigram factor (top line) predicts that the reading time of the region that ends with word 6 depends on word 5, but the probability of word 5 given its context is never included in the model, so an improbable transition between words 4 and 5 would not be caught.


In contrast, a status-based network theory would penalize non-transitive triads such as β >>< .

in an unsupervised model, we can examine the weights to learn about the semantics of the induced edge types, and to see which theory best describes the signed network configurations that follow from the linguistic signal.


Because each such transformation is represented in the high-dimensional embedding space, it therefore captures the semantics of the change.

it allows us to build vector representations for any unseen word for which a morphological analysis is found, therefore covering an unbounded (albeit incomplete) vocabulary.


This step is done using off-the-shelf analyzers such as Morfessor (Creutz and Lagus, 2007).

the morphological analysis happens within a different model compared to the model in which the resulting morphemes are consequently used.


In contrast, the work presented here uses the same vector-space embedding to achieve both the morphological analysis of words and to compute their representation.

the morphological analysis can be justified in terms of the relationship between the resulting representation and other words that exhibit similar morphological properties.


We assume that the statistics for unseen words (with respect to their morphological makeup) are similar with the statistics for low-frequency words.

for some relatively-low counts L and H, the is a good proxy for the population of OOV words that we see at runtime.


It should be noted that because this method counts all subtrees, it is capable of selecting overlapping phrases like the methods based on n-grams.

we also experiment with a method using together both subtrees and the semi-maximal phrases proposed in Section 4.1 to select both syntactic and non-redundant segments.


As our experiments will show, under NCE with translation noise, the NNJM no longer provides meaningful scores for the entire vocabulary.

we also experiment with a novel mixture noise distribution: We deviate from their configuration by using a single 512-node hidden layer, motivated by our internal development experiments.


The spread between the worst and best NNJM is only 0.3 BLEU under General, while it is 0.8 BLEU under Adapted.

groups training unadapted models may not see as large drops from NCE-U as we have reported above.


As our task is to emphasize the main points made in the input, some of them can be removed without losing too much information.

we define an importance score f (x k ) ∈ [0, 1] for each document x k (see Section 3.5).


We therefore need to trade-off the discovery of unique facets of an entity with those which are common to the entities being compared.

while clusters that balance the entities are preferable, it is also acceptable to have clusters where one of the entities is sparsely represented (or not represented at all).


We construct a vector representation for each descriptive phrase by averaging the word-vectors of individual words in a phrase (Mikolov et al., 2013) 4 .

this model is pLSA with each topic-word distribution represented as a Gaussian distribution over descriptive phrases in the embedding space.


Entity-Balanced Gaussian pLSA: Vanilla Gaussian pLSA may not always lead to a good clustering for comparison since the expression above does not involve any term to balance the entity-information in clusters, as motivated earlier.

we incorporate a regularizer term to have a good balance (proportion) of entities in each cluster (see Figure 2 (right) (c)) resulting in our final model for comparison called Entity-Balanced Gaussian pLSA (EB G-pLSA).


In addition, users also tend to compare and contrast while reviewing movies so there are even references to other movies.

the descriptive phrases extracted were much more noisy.


To our knowledge there are no other automated systems comparing cities for tourism (or movies), hence we could not evaluate our system against existing approaches.

we decided to evaluate the benefit of the output generated by our system (i.e., comparison tables) against reading the original WikiTravel articles.


The output of convolution is given by: where b k j is the j-th column of the matrix B k .

the d dimensional aggregate embedding vector z i is defined as z i = [z i1 , .


Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains.

it is important to leverage existing resources and exploit similarities between domains to facilitate domain adaptation.


A close look at the results have shown that in those cases, the patient roles are often specified by pronouns.

the track label features and linguistic features are not very helpful, and the correct grounding mainly depends on visual features.


The change of state can be perceived from the physical world.

another direction is to systematically study causality of verbs.


(2011) hypothesized that metaphor is commonly used to describe abstract concepts in terms of more concrete or physical experiences.

turney and colleagues expected that there would be some discrepancy in the level of concrete-ness of source and target terms in the metaphor.


The second term φ is a consistency term that measures the compatibility of a generalized relation t with the relations in S. We assume that if a relation is missing from S, then it is false (this corresponds to a closed world assumption over the domain of S).

φ is the ratio of the scores of relations in S consistent with relation t (i.e.


RNNGs can be combined with a particle filter inference scheme (rather than the importance sampling method based on a discriminative parser, §5) to produce a left-to-right marginalization algorithm that runs in expected linear time.

they could be used in applications that require language models.


), sentences are represented as strings of characters without similar natural delimiters.

it is generally claimed that the first step in a Chinese language processing task is to identify the sequence of words in a sentence and mark * Work done at Nuance during an internship.


In the example (1), the same span of text (the input) can convey entirely opposite meanings (the English sentences in parentheses) depending on how word boundaries (CWS 1 and CWS 2) are labeled.

it is generally believed that more accurate word segmentations should benefit more the subsequent Chinese language processing tasks, such as part-of-speech tagging, named entity recognition, etc.


We explored various ways of systematically discovering and unifying latent and expressed expectations from nontraditional resources: • Language Universals: Language-independent rules and patterns; • Native Speaker: Interaction with native speakers through a machine-readable survey and supervised active learning; • Prior Mining: IL entity prior knowledge mining from both mono-lingual and cross-lingual corpora and knowledge bases; Furthermore, in emergent situations these expectations might not be available at once, and they may have different costs, so we need to organize and prioritize them to yield optimal performance within given time bounds.

we also experimented with various cost-aware composition methods with the input of acquired expectations, plus a time bound for development (1 hour, 2 hours), and the output as a wall-time schedule that determines the best sequence of applying modules and maximizes the use of all available resources.


We use ϕ SE to represent how informative a sentence is: , where T is the length of x, m ranges over all possible token labels and P θ (y t = m) is the probability when y t is tagged as m. A new requirement for IL name tagging is a Linguistic Workflow Generator, which can generate an activity schedule to organize and maximize the use of acquired expectations to yield optimal F-scores within given time bounds.

the input to the IL name tagger is not only the test data, but also a time bound for development (1 hour, 2 hours, 24 hours, 1 week, 1 month, etc.).


, v, r y ), where r y is a learned embedding of a label type.

the output buffer contains a single vector representation for each labeled chunk that is generated, regardless of its length.


Our second intuition is that names, which may individually be quite varied, appear in regular contexts in large corpora.

we use embed- dings learned from a large corpus that are sensitive to word order (4.2).


Recurrent models like RNNs and LSTMs are capable of encoding very long sequences, however, they have a representation biased towards their most recent inputs.

we expect the final representation of the forward LSTM to be an accurate representation of the suffix of the word, and the final state of the backward LSTM to be a better representation of its prefix.